Important Dates

Submission of extended abstracts: May 19, 2010 (later submission might not be considered for review)

Notification of acceptance: May 28, 2010

Workshop date: June 27th, 2010

Overview

The ability to adapt to changing environment autonomously will be
essential for future robots. While this need is well-recognized,
most machine learning research focuses largely on perception and
static data sets. Instead, future robots need to interact with
the environment to generate the data that is needed to foster
real-time adaptation based on all information collected in
previous interactions and observations. In other words, we need
to close the loop between the robot acting, robot sensing and
robot learning. Novel active methods need to outperform passive
methods by a margin that compensates the potential the extra
computational burden and the cost of the active data sampling.

During the last years, there has been an increasing interest in
related techniques that could potentially become applicable in
this context. These include techniques from statistics such as
adaptive sensing or sequential experimental design as well novel
reinforcement learning methods that have the potential to scale
into robotics. In this context, we would like to bring together
researchers from both the robotics and active machine learning in
order to discuss for which problems the autonomous learning loop
can be closed using learning, and to identify the machine
learning methods that can be used to close it.